The number of students selected can be influenced by, for example, staff capacity and the availability of resources within the Department, as well as academic merit and University transformation objectives. As staff capacity and resources can fluctuate from year to year, the number of students selected can also differ from year to year.
If the Statistics background of the applicant is deemed insufficient after a case-by-case determination by the Department of Statistics and Actuarial Science, the Department may require an additional departmental assessment on third-year level Statistics topics. Students may also be required to complete additional undergraduate Stellenbosch University Statistics modules along with their honours studies.
Students will be required to pass modules totalling at least 120 credits made up as follows:
| Modules | Code | Semester | Credits |
|---|---|---|---|
| Applied Time Series Analysis | 10748-722 | 1 | 12 |
| Applied Stochastic Simulation | 65269-746 | 2 | 12 |
| Biostatistics | 10408-712 | 1 | 12 |
| Capita Selecta in Statistics A* | 11920-725 | NA | 12 |
| Capita Selecta in Statistics B | 11921-755 | NA | 12 |
| Experimental Design | 10440-713 | 1 | 12 |
| Introduction to R Programming | 10547-723 | 1 | 6 |
| Multivariate Statistical Analysis A | 10600-721 | 1 | 12 |
| Multivariate Statistical Analysis B | 10600-751 | 2 | 12 |
| Sampling Techniques | 10705-742 | 1 | 12 |
| Stochastic Modelling | 65242-736 | 2 | 12 |
| Research Assignment: Statistics | 11226-792 | 1 & 2 | 30 |
NA – This module is not presented in 2024.
* – Topics in Data Science – presented under Capita Selecta
Please take note of the following prerequisite:
Multivariate Methods in Statistics A 721(12) is a prerequisite for Multivariate Methods in Statistics B 751(12).
Objectives and content
This module is an introduction to programming and data analysis within the R open source environment. It is presented as a block course in the first two weeks of the first semester and commences the week preceding general commencement of classes. The viewpoint of this module as well as of all modules where R plays a role is in agreement with the aim of the R computer language: “R has a simple goal: To turn ideas into software, quickly and faithfully”.
Objectives and content
Biostatistics may be regarded as the study of the application of statistics to medicine. It covers medical terminology, the design of clinical trials, the collection and numerical analysis of data, the interpretation of the analyses and the drawing of conclusions. Particular emphasis is given to skills relevant to medical literature (the writing, as well as the understanding of writing by others) and statistical techniques and software that are widely used when doing medical research. It is not a mathematically strenuous course. It deals primarily with the philosophy and terminology of medical research, as well as the statistical techniques problems encountered in the medical field in particular. Topics that will be covered are: SAS,Clinical trials, Power and sample size analysis, Longitudinal data analysis, Handling missing data and Statistical genetics.
Objectives and content
Data collected in practice rarely consist of one isolated variable. Mostly, data consist of many variables influencing one another. If only one variable upon a time is singled out for analysis, the data analyst is in danger of arriving at completely wrong conclusions. Multivariate statistical analysis entails the study of techniques for analysing data sets consisting of various variables influencing one another. This model aims to provide students with the expertise to confidently come to the right conclusions when analysing multivariate data. Students need to complete the A module before the B module can be taken.
Objectives and content
This module does not require advanced mathematics and is an option for both statistics and mathematical statistics students. Focus is mainly on the practical implementation of techniques together with computer packages from consultancy perspective. Attention is given to modeling, design matrices, least squares and diagnostics.
Objectives and content
The design of a sample is one of the most important aspects of any survey: no amount of statistical analysis can compensate for a badly-designed sample. Therefore, the emphasis of this course is the scientific design of samples, determination of sample sizes and is related to methods for analysing the data from a survey. Contents include: Questionnaire design, sampling techniques (simple random, stratified, systematic, cluster, complex), proportional vs disproportional allocation for stratified sampling, ratio and regression estimation, estimation of means, totals proportions and their variances, weighting of survey data, dealing with non-response.
Objectives and content
In this module an introduction is given to Extreme Value Theory (EVT) and its role in Financial Risk Management. EVT entails the study of extreme events and for this theory has been developed to describe the behaviour in the tails of distributions. The module will disduss the theory in a conceptual fashion without proving the results. It will be shown how this theory can be used to carry out inferences on the relevant parameters of the underlying distribution. Both the classical approach of block maxima based on the Fisher-Tippett Theorem and the more modern threshold approach based on the Pickands-Balkema-de Haan Theorem will be discussed and applied. Results for both independent and dependent data will be covered.
Objectives and content
This module is a continuation of undergraduate time series analyses and concentrates on more advanced forecasting techniques. Topics that are covered include:
Objectives and content
In this module the student learns to understand and apply the underlying mathematical principles behind stochastic simulation. Statistical theory is applied to practical problems which are modelled on computer. To achieve this, the student revises probability theory, including conditional probability and independence, discrete and continuous distributions, probability integral transformation, and Bayes’ theorem. Pseudorandom number generation is studied to simulate stochastic problems. These pseudorandom numbers are used to evaluate integrals, and to generate discrete and continuous random variables. It is also important to do a statistical analysis of simulated data using point and interval estimates of the mean and variance, as well as bootstrap techniques. Input data for simulation models are analysed with the chi-squared and Kolmogorov-Smirnoff goodness-of-fit tests. The student finally develops practical simulation models of discrete-event, dynamic stochastic processes using a dedicated simulation software package.
Objectives and content
Selected and specialised topics to be followed in Mathematical Statistics. Content varies from year to year when offered.
Please note: An application has been submitted to externally amend the title of this programme to Master of Commerce in Applied Statistics and Data Science – abbreviation” MCom (Applied Statistics and Data Science)”. This change will be implemented once the amended title has been approved by the Department of Higher Education and Training (DHET) and the Council on Higher Education (CHE), and the change has been registered by the South African Qualifications Authority (SAQA).
An honours degree in Mathematical Statistics with an average mark of at least 65%.
The number of students selected can be influenced by, for example, staff capacity and the availability of resources within the Department, as well as academic merit and University transformation objectives. As staff capacity and resources can fluctuate from year to year, the number of students selected can also differ from year to year.
If the Statistics background of the applicant is deemed insufficient after a case-by-case determination by the Department of Statistics and Actuarial Science, the Department may require an additional departmental assessment on Statistics topics. Students may also be required to complete additional Stellenbosch University Statistics modules along with the MCom studies.
You can choose between two possible options:
| Module | Code | Semester | Credits |
|---|---|---|---|
| Advanced Sampling Techniques | 10523-818 | NA | 15 |
| Advanced Statistics A | 10521-821 | NA | 15 |
| Advanced Statistics B | 10522-851 | NA | 15 |
| Multi-dimensional Scaling A | 18130-822 | NA | 15 |
| Multi-dimensional Scaling B | 11910-852 | NA | 15 |
| Bootstrap and other Resampling Techniques A | 10694-811 | 1 | 15 |
| Bootstrap and other Resampling Techniques B | 10695-841 | 2 | 15 |
| Thesis: Statistics Compulsory with 879 | 11244-891 | 1 & 2 | 90 |
| Research Assignment: Statistics Compulsory with 889 | 11226-893 | 1 & 2 | 60 |
NA – This module is not presented in 2024.
The PhD programme in Statistics have a minimum residency of 2 years. Degrees are awarded on the basis of independent research providing an original distinct contribution to the methodological field of Statistics. The straightforward statistical analysis of a data set from an applied field might qualify for a PhD in the applied field but does not warrant a PhD in Mathematical Statistics.
Applicants must have completed a Masters degree in Statistics with evidence of having passed accredited Masters-level courses in the field where the PhD research will be performed.
If an applicant is interested in pursuing a PhD with potential supervisors who are members of MuViSU the following requirement will apply to applicants who did not complete a Masters degree in Statistics at SU:
Only applicants who obtain an average for at least 65% for the assignments will be considered.
Applications are submitted on the SUNstudent application portal.
The following documents are submitted as part of the application:
Applicants are advised to consult the personal research webpages of staff members to establish the possible theoretical research themes in line with the research of potential supervisors.
GEM is managed as a unit in the Dean’s office. It started its operations in 2014 with the purpose of strengthening the Faculty’s doctoral throughput rate by allowing some students to study full-time and enhancing access to doctoral studies in the disciplines that are housed in the Faculty of Economic and Management Sciences. GEM essentially plays a supporting role so that candidates have a better chance of finalising their doctoral studies within the allocated time of three years. Find out more on the GEM website.
The admission requirements for students that are admitted into GEM’s doctoral programme are the same as the requirements stipulated for the PhD degree.
Applications are accepted during the application period that is indicated on the GEM website under Research Themes.
Dr Jaco Franken
Manager: Graduate School of Economic and Management Sciences (GEM)
Room 1017, AI Perold building
Stellenbosch University
Tel: 021 808 9545
E-mail: franken@sun.ac.za
Website: https://www.sun.ac.za/english/faculty/economy/gem